Assessing Measures of Glycemic Variability

ABSTRACT

Methods, devices and systems for receiving an instruction to determine a glycemic variation level, retrieving a stored metric for determining the glycemic variation level, retrieving one or more parameters associated with the retrieved metric analysis, determining the glycemic variation level based on the retrieved one or more parameters for the retrieved metric analysis, and outputting the determined glycemic variation level when it is determined that the retrieved one or more parameters associated with the retrieved metric analysis meets a predetermined condition are disclosed.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/854,005 filed Mar. 29, 2013, which is a continuation of U.S.patent application Ser. No. 12/257,353 filed Oct. 23, 2008, now U.S.Pat. No. 8,409,093, entitled “Assessing Measures of GlycemicVariability”, which claims priority under 35 U.S.C. §119(e) to U.S.Provisional Application No. 60/982,110 filed Oct. 23, 2007, entitled“Assessing Measures Of Glycemic Variability”, and assigned to theAssignee of the present application, Abbott Diabetes Care Inc. ofAlameda, Calif., the disclosures of each of which are incorporatedherein by reference for all purposes.

BACKGROUND

Analyte, e.g., glucose monitoring systems including continuous anddiscrete monitoring systems generally include a small, lightweightbattery powered and microprocessor controlled system which is configuredto detect signals proportional to the corresponding measured glucoselevels using an electrometer, and RF signals to transmit the collecteddata. One aspect of certain analyte monitoring systems include atranscutaneous or subcutaneous analyte sensor configuration which is,for example, partially mounted on the skin of a subject whose analytelevel is to be monitored. The sensor cell may use a two orthree-electrode (work, reference and counter electrodes) configurationdriven by a controlled potential (potentiostat) analog circuit connectedthrough a contact system.

The analyte sensor may be configured so that at least a portion thereofis placed under the skin of the patient so as to detect the analytelevels of the patient. In embodiments in which a portion is below theskin and a portion is above, the portion above the skin may be directlyor indirectly connected with the transmitter unit. The transmitter unitis configured to transmit the analyte levels, e.g., in the form ofcurrent, detected by the sensor over a wireless (or wired) communicationlink such as an RF (radio frequency) communication link to areceiver/monitor unit. The receiver/monitor unit performs data analysis,among others on the received analyte levels to generate informationpertaining to the monitored analyte levels.

To obtain accurate data from the analyte sensor, calibration may benecessary. In certain instances, blood glucose measurements areperiodically obtained using, for example, a conventional analyte teststrip and blood glucose meter, and the measured blood glucose values areused to calibrate the sensors. Indeed, the patient may calibrate eachnew analyte sensor using for example, capillary blood glucosemeasurements. Due to a lag factor between the monitored data and themeasured blood glucose values, an error may be introduced in themonitored data.

In view of the foregoing, it would be desirable to have a method andsystem for calibrating analyte sensors of an analyte monitoring systemto account for such lag errors in analyte monitoring systems.

SUMMARY

In particular embodiments, methods, devices and systems for receiving aninstruction to determine a glycemic variation level, retrieving a storedmetric for determining the glycemic variation level, retrieving one ormore parameters associated with the retrieved metric analysis,determining the glycemic variation level based on the retrieved one ormore parameters for the retrieved metric analysis, and outputting thedetermined glycemic variation level when it is determined that theretrieved one or more parameters associated with the retrieved metricanalysis meets a predetermined condition are disclosed.

These and other objects, features and advantages of the presentdisclosure will become more fully apparent from the following detaileddescription of the embodiments, the appended claims and the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a data monitoring and managementsystem for practicing one or more embodiments of the present disclosure;

FIG. 2 is a block diagram of the transmitter unit of the data monitoringand management system shown in FIG. 1 in accordance with one embodimentof the present disclosure;

FIG. 3 is a block diagram of the receiver/monitor unit of the datamonitoring and management system shown in FIG. 1 in accordance with oneembodiment of the present disclosure;

FIG. 4 is a flowchart illustrating glycemic variability determination inaccordance with one aspect of the present disclosure;

FIG. 5 is a flowchart illustrating glycemic variability determination inaccordance with another aspect of the present disclosure;

FIG. 6 shows a sample subject estimation of metric analysis based onMAGE with decreasing excursions in one aspect;

FIG. 7 shows a sample subject estimation of metric analysis based onGRADE in one aspect;

FIG. 8 illustrates the overall glucose variability measures andestimation requirements for the experimental study;

FIG. 9 illustrates the number of days of continuously monitored glucosedata to attain the estimation requirement;

FIG. 10 illustrates the glucose standard deviation analysis as themetric for glycemic variability assessment;

FIG. 11 illustrates the proportion of time analysis (in hours per day—inhypoglycemia or hyperglycemia) as the metric for glycemic variabilityassessment;

FIG. 12 illustrates the number of episodes per day analysis (inhypoglycemia and hyperglycemia) as the metric for glycemic variabilityassessment;

FIG. 13 illustrates the maximum excursion analysis for each hypoglycemicepisode or hyperglycemic episode as the metric for glycemic variabilityassessment;

FIG. 14 illustrates the results of MAGE analysis as the metric forglycemic variability assessment;

FIG. 15 illustrates the results of the Lability Index analysis as themetric for glycemic variability assessment;

FIG. 16 illustrates the results of Kovachev Risk Score analysis as themetric for glycemic variability assessment; and

FIG. 17 illustrates the results of GRADE analysis as the metric forglycemic variability assessment.

DETAILED DESCRIPTION

As described in further detail below, in accordance with the variousembodiments of the present disclosure, there is provided a method andsystem for performing glycemic variability assessment based on one ormore metric analysis to provide a snapshot of a diabetic condition basedon continuously monitored glucose data over a predetermined time period.

FIG. 1 illustrates a data monitoring and management system such as, forexample, analyte (e.g., glucose) monitoring system 100 in accordancewith one embodiment of the present disclosure. The subject invention isfurther described primarily with respect to a glucose monitoring systemfor convenience and such description is in no way intended to limit thescope of the invention. It is to be understood that the analytemonitoring system may be configured to monitor a variety of analytes,e.g., lactate, and the like. For example, analytes that may be monitoredinclude but are not limited to, for example, acetyl choline, amylase,bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g.,CK-MB), creatine, DNA, fructosamine, glucose, glutamine, growthhormones, hormones, ketones, lactate, peroxide, prostate-specificantigen, prothrombin, RNA, thyroid stimulating hormone, and troponin.The concentration of drugs, such as, for example, antibiotics (e.g.,gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs ofabuse, theophylline, and warfarin, may also be monitored.

The analyte monitoring system 100 includes a sensor 101, a transmitterunit 102 directly or indirectly coupled to the sensor 101, and a primaryreceiver unit 104 which is configured to communicate with thetransmitter unit 102 via a communication link 103. The primary receiverunit 104 may be further configured to transmit data to a data processingterminal 105 for evaluating the data received by the primary receiverunit 104. Moreover, the data processing terminal in one embodiment maybe configured to receive data directly from the transmitter unit 102 viaa communication link 106 which may optionally be configured forbi-directional communication.

Also shown in FIG. 1 is an optional secondary receiver unit 106 which isoperatively coupled to the communication link and configured to receivedata transmitted from the transmitter unit 102. Moreover, as shown inthe Figure, the secondary receiver unit 106 is configured to communicatewith the primary receiver unit 104 as well as the data processingterminal 105. Indeed, the secondary receiver unit 106 may be configuredfor bi-directional wireless communication with each of the primaryreceiver unit 104 and the data processing terminal 105. As discussed infurther detail below, in one embodiment of the present disclosure, thesecondary receiver unit 106 may be configured to include a limitednumber of functions and features as compared with the primary receiverunit 104. As such, the secondary receiver unit 106 may be configuredsubstantially in a smaller compact housing or embodied in a device suchas a wrist watch, for example. Alternatively, the secondary receiverunit 106 may be configured with the same or substantially similarfunctionality as the primary receiver unit 104, and may be configured tobe used in conjunction with a docking cradle unit for placement bybedside, for night time monitoring, and/or bi-directional communicationdevice.

Only one sensor 101, transmitter unit 102, communication link 103, anddata processing terminal 105 are shown in the embodiment of the analytemonitoring system 100 illustrated in FIG. 1. However, it will beappreciated by one of ordinary skill in the art that the analytemonitoring system 100 may include one or more sensor 101, transmitterunit 102, communication link 103, and data processing terminal 105.Moreover, within the scope of the present disclosure, the analytemonitoring system 100 may be a continuous, semi-continuous, or adiscrete monitoring system. In a multi-component environment, eachdevice is configured to be uniquely identified by each of the otherdevices in the system so that communication conflict is readily resolvedbetween the various components within the analyte monitoring system 100.

In one embodiment of the present disclosure, the sensor 101 isphysically positioned in or on the body of a user whose analyte level isbeing monitored. In one aspect, the sensor 101 may be configured to useone or more of coulometric, amperometric, potentiometric orconductimetric approaches to measure the analyte level being monitored.The sensor 101 may be configured to continuously sample the analyte ofthe user and the sampled analyte may be converted into a correspondingdata signal for transmission by the transmitter unit 102. In oneembodiment, the transmitter unit 102 is mounted on the sensor 101 sothat both devices are positioned on the user's body. The transmitterunit 102 performs data processing such as filtering and encoding on datasignals, each of which corresponds to a sampled analyte level of theuser, for transmission to the primary receiver unit 104 via thecommunication link 103.

In one embodiment, the analyte monitoring system 100 is configured as aone-way RF communication path from the transmitter unit 102 to theprimary receiver unit 104. In such embodiment, the transmitter unit 102may transmit the sampled data signals received from the sensor 101without acknowledgement from the primary receiver unit 104 that thetransmitted sampled data signals have been received (in otherembodiments there may be acknowledgement). For example, the transmitterunit 102 may be configured to transmit the encoded sampled data signalsat a fixed rate (e.g., at one minute intervals or other interval) afterthe completion of the initial power-on procedure. Likewise, the primaryreceiver unit 104 may be configured to detect such transmitted encodedsampled data signals at predetermined time intervals. Alternatively, theanalyte monitoring system 100 may be configured with a bi-directional RF(or otherwise) communication between the transmitter unit 102 and theprimary receiver unit 104.

Additionally, in one aspect, the primary receiver unit 104 may includetwo sections. The first section is an analog interface section that isconfigured to communicate with the transmitter unit 102 via thecommunication link 103. In one embodiment, the analog interface sectionmay include a RF receiver and an antenna for receiving and amplifyingthe data signals from the transmitter unit 102, which are thereafter,demodulated with a local oscillator and filtered through a band-passfilter. The second section of the primary receiver unit 104 is a dataprocessing section which is configured to process the data signalsreceived from the transmitter unit 102 such as by performing datadecoding, error detection and correction, data clock generation, anddata bit recovery.

In operation in certain embodiments, upon completing a power-onprocedure if required, the primary receiver unit 104 is configured todetect the presence of the transmitter unit 102 within its range basedon, for example, the strength of the detected data signals received fromthe transmitter unit 102 or a predetermined transmitter identificationinformation. Upon successful synchronization with the correspondingtransmitter unit 102, the primary receiver unit 104 is configured tobegin receiving from the transmitter unit 102 data signals correspondingto the user's detected analyte level. More specifically, the primaryreceiver unit 104 in one embodiment is configured to performsynchronized time hopping with the corresponding synchronizedtransmitter unit 102 via the communication link 103 to obtain the user'sdetected analyte level.

Referring again to FIG. 1, the data processing terminal 105 may includea personal computer, a portable computer such as a laptop or a handhelddevice (e.g., personal digital assistants (PDAs) telephone such as acellular telephone), and the like, each of which may be configured fordata communication with the receiver via a wired or a wirelessconnection. Additionally, the data processing terminal 105 may furtherbe connected to a data network (not shown) for storing, retrieving andupdating data corresponding to the detected analyte level of the user.

Within the scope of the present disclosure, the data processing terminal105 may include an infusion device such as an insulin infusion pump orthe like, which may be configured to administer a drug such as, forexample insulin, to users, and which may be configured to communicatewith the receiver unit 104 for receiving, among others, the measuredanalyte level. Alternatively, the receiver unit 104 may be configured tointegrate an infusion device therein so that the receiver unit 104 isconfigured to administer insulin therapy to patients, for example, foradministering and modifying basal profiles, as well as for determiningappropriate boluses for administration based on, among others, thedetected analyte levels received from the transmitter unit 102.

Additionally, the transmitter unit 102, the primary receiver unit 104and the data processing terminal 105 may each be configured forbi-directional wireless communication such that each of the transmitterunit 102, the primary receiver unit 104 and the data processing terminal105 may be configured to communicate (that is, transmit data to andreceive data from) with each other via the wireless communication link103. More specifically, the data processing terminal 105 may in oneembodiment be configured to receive data directly from the transmitterunit 102 via the communication link 106, where the communication link106, as described above, may be configured for bi-directionalcommunication.

In this embodiment, the data processing terminal 105 which may includean insulin pump, may be configured to receive the analyte signals fromthe transmitter unit 102, and thus, incorporate the functions of thereceiver 104 including data processing for managing the patient'sinsulin therapy and analyte monitoring. In one embodiment, thecommunication link 103 may include one or more of an RF communicationprotocol, an infrared communication protocol, a Bluetooth® enabledcommunication protocol, an 802.11x wireless communication protocol, oran equivalent wireless communication protocol which would allow secure,wireless communication of several units (for example, per HIPAArequirements) while avoiding potential data collision and interference.

FIG. 2 is a block diagram of the transmitter of the data monitoring anddetection system shown in FIG. 1 in accordance with one embodiment ofthe present disclosure. Referring to the Figure, the transmitter unit102 in one embodiment includes an analog interface 201 configured tocommunicate with the sensor 101 (FIG. 1), a user input 202, and atemperature detection section 203, each of which is operatively coupledto a transmitter processor 204 such as a central processing unit (CPU).As can be seen from FIG. 2, there are provided (this is veryspecific—what if more or less contacts—no guard, etc.) four contacts,three of which are electrodes—work electrode (W) 210, guard contact (G)211, reference electrode (R) 212, and counter electrode (C) 213, eachoperatively coupled to the analog interface 201 of the transmitter unit102 for connection to the sensor unit 201 (FIG. 1). In one embodiment,each of the work electrode (W) 210, guard contact (G) 211, referenceelectrode (R) 212, and counter electrode (C) 213 may be made using aconductive material that may be applied in any suitable manner, e.g.,printed or etched, for example, such as carbon, gold, and the like,which may be printed, or metal foil (e.g., gold) which may be etched.

Further shown in FIG. 2 are a transmitter serial communication section205 and an RF transmitter 206, each of which is also operatively coupledto the transmitter processor 204. Moreover, a power supply 207 such as abattery is also provided in the transmitter unit 102 to provide thenecessary power for the transmitter unit 102. Additionally, as can beseen from the Figure, clock 208 is provided to, among others, supplyreal time information to the transmitter processor 204.

In one embodiment, a unidirectional input path is established from thesensor 101 (FIG. 1) and/or manufacturing and testing equipment to theanalog interface 201 of the transmitter unit 102, while a unidirectionaloutput is established from the output of the RF transmitter 206 of thetransmitter unit 102 for transmission to the primary receiver unit 104.In this manner, a data path is shown in FIG. 2 between theaforementioned unidirectional input and output via a dedicated link 209from the analog interface 201 to serial communication section 205,thereafter to the processor 204, and then to the RF transmitter 206. Assuch, in one embodiment, via the data path described above, thetransmitter unit 102 is configured to transmit to the primary receiverunit 104 (FIG. 1), via the communication link 103 (FIG. 1), processedand encoded data signals received from the sensor 101 (FIG. 1).Additionally, the unidirectional communication data path between theanalog interface 201 and the RF transmitter 206 discussed above allowsfor the configuration of the transmitter unit 102 for operation uponcompletion of the manufacturing process as well as for directcommunication for diagnostic and testing purposes.

As discussed above, the transmitter processor 204 is configured totransmit control signals to the various sections of the transmitter unit102 during the operation of the transmitter unit 102. In one embodiment,the transmitter processor 204 also includes a memory (not shown) forstoring data such as the identification information for the transmitterunit 102, as well as the data signals received from the sensor 101. Thestored information may be retrieved and processed for transmission tothe primary receiver unit 104 under the control of the transmitterprocessor 204. Furthermore, the power supply 207 may include acommercially available battery.

The transmitter unit 102 is also configured such that the power supplysection 207 is capable of providing power to the transmitter for apredetermined minimum continuous operation time period and also with apredetermined minimum shelf life time period such as, for example, aminimum of about three months of continuous operation after having beenstored for about eighteen months in a low-power (non-operating) mode. Inone embodiment, this may be achieved by the transmitter processor 204operating in low-power modes in the non-operating state, for example,drawing no more than approximately 1 μA of current. Indeed, in oneembodiment, the final step during the manufacturing process of thetransmitter unit 102 may place the transmitter unit 102 in thelow-power, non-operating state (i.e., post-manufacture sleep mode). Inthis manner, the shelf life of the transmitter unit 102 may besignificantly improved. Moreover, as shown in FIG. 2, while the powersupply unit 207 is shown as coupled to the processor 204, and as such,the processor 204 is configured to provide control of the power supplyunit 207, it should be noted that within the scope of the presentdisclosure, the power supply unit 207 is configured to provide thenecessary power to each of the components of the transmitter unit 102shown in FIG. 2.

Referring back to FIG. 2, the power supply section 207 of thetransmitter unit 102 in one embodiment may include a rechargeablebattery unit that may be recharged by a separate power supply rechargingunit (for example, provided in the receiver unit 104) so that thetransmitter unit 102 may be powered for a longer period of usage time.Moreover, in one embodiment, the transmitter unit 102 may be configuredwithout a battery in the power supply section 207, in which case thetransmitter unit 102 may be configured to receive power from an externalpower supply source (for example, a battery) as discussed in furtherdetail below.

Referring yet again to FIG. 2, the optional temperature detectionsection 203 of the transmitter unit 102 is configured to monitor thetemperature of the skin near the sensor insertion site. The temperaturereading may be used to adjust the analyte readings obtained from theanalog interface 201. The RF transmitter 206 of the transmitter unit 102may be configured for operation in the frequency band of 315 MHz to 433MHz, for example, in the United States. Further, in one embodiment, theRF transmitter 206 is configured to modulate the carrier frequency byperforming Frequency Shift Keying and Manchester encoding. In oneembodiment, the data transmission rate is 19,200 symbols per second,with a minimum transmission range for communication with the primaryreceiver unit 104.

Referring yet again to FIG. 2, also shown is a leak detection circuit214 coupled to the guard electrode (G) 211 and the processor 204 in thetransmitter unit 102 of the data monitoring and management system 100.The leak detection circuit 214 in accordance with one embodiment of thepresent disclosure may be configured to detect leakage current in thesensor 101 to determine whether the measured sensor data are corrupt orwhether the measured data from the sensor 101 is accurate.

FIG. 3 is a block diagram of the receiver/monitor unit of the datamonitoring and management system shown in FIG. 1 in accordance with oneembodiment of the present disclosure. Referring to FIG. 3, the primaryreceiver unit 104 includes a blood glucose test strip interface 301, anRF receiver 302, an input 303, a temperature detection section 304, anda clock 305, each of which is operatively coupled to a receiverprocessor 307. As can be further seen from the Figure, the primaryreceiver unit 104 also includes a power supply 306 operatively coupledto a power conversion and monitoring section 308. Further, the powerconversion and monitoring section 308 is also coupled to the receiverprocessor 307. Moreover, also shown are a receiver serial communicationsection 309, and an output 310, each operatively coupled to the receiverprocessor 307.

In one embodiment, the test strip interface 301 includes a glucose leveltesting portion to receive a manual insertion of a glucose test strip,and thereby determine and display the glucose level of the test strip onthe output 310 of the primary receiver unit 104. This manual testing ofglucose can be used to calibrate sensor 101. The RF receiver 302 isconfigured to communicate, via the communication link 103 (FIG. 1) withthe RF transmitter 206 of the transmitter unit 102, to receive encodeddata signals from the transmitter unit 102 for, among others, signalmixing, demodulation, and other data processing. The input 303 of theprimary receiver unit 104 is configured to allow the user to enterinformation into the primary receiver unit 104 as needed. In one aspect,the input 303 may include one or more keys of a keypad, atouch-sensitive screen, or a voice-activated input command unit. Thetemperature detection section 304 is configured to provide temperatureinformation of the primary receiver unit 104 to the receiver processor307, while the clock 305 provides, among others, real time informationto the receiver processor 307.

Each of the various components of the primary receiver unit 104 shown inFIG. 3 is powered by the power supply 306 which, in one embodiment,includes a battery. Furthermore, the power conversion and monitoringsection 308 is configured to monitor the power usage by the variouscomponents in the primary receiver unit 104 for effective powermanagement and to alert the user, for example, in the event of powerusage which renders the primary receiver unit 104 in sub-optimaloperating conditions. An example of such sub-optimal operating conditionmay include, for example, operating the vibration output mode (asdiscussed below) for a period of time thus substantially draining thepower supply 306 while the processor 307 (thus, the primary receiverunit 104) is turned on. Moreover, the power conversion and monitoringsection 308 may additionally be configured to include a reverse polarityprotection circuit such as a field effect transistor (FET) configured asa battery activated switch.

The serial communication section 309 in the primary receiver unit 104 isconfigured to provide a bi-directional communication path from thetesting and/or manufacturing equipment for, among others,initialization, testing, and configuration of the primary receiver unit104. Serial communication section 104 can also be used to upload data toa computer, such as time-stamped blood glucose data. The communicationlink with an external device (not shown) can be made, for example, bycable, infrared (IR) or RF link. The output 310 of the primary receiverunit 104 is configured to provide, among others, a graphical userinterface (GUI) such as a liquid crystal display (LCD) for displayinginformation. Additionally, the output 310 may also include an integratedspeaker for outputting audible signals as well as to provide vibrationoutput as commonly found in handheld electronic devices, such as mobiletelephones presently available. In a further embodiment, the primaryreceiver unit 104 also includes an electro-luminescent lamp configuredto provide backlighting to the output 310 for output visual display indark ambient surroundings.

Referring back to FIG. 3, the primary receiver unit 104 in oneembodiment may also include a storage section such as a programmable,non-volatile memory device as part of the processor 307, or providedseparately in the primary receiver unit 104, operatively coupled to theprocessor 307. The processor 307 may be configured to perform Manchesterdecoding as well as error detection and correction upon the encoded datasignals received from the transmitter unit 102 via the communicationlink 103.

Additional detailed description of the continuous analyte monitoringsystem, its various components including descriptions of thetransmitter, the receiver/display unit, data communication, calibrationand sensor insertion, among others, are provided in U.S. Pat. No.6,175,752 issued Jan. 16, 2001 entitled “Analyte Monitoring Device andMethods of Use”, and in application Ser. No. 10/745,878 filed Dec. 26,2003 entitled “Continuous Glucose Monitoring System and Methods of Use”,each assigned to the Assignee of the present application, disclosure ofeach of which are incorporated herein by reference for all purposes.

In aspects of the present disclosure, a robust glycemic variabilitydetermination function is provided. In particular, in one aspect, basedon the analyte data collected over a predetermined time period, glycemicvariability may be determined using one or more defined metrics foranalysis to provide, for example, a diabetic condition of a patient or auser of the analyte monitoring system at any given time. In one aspect,one or more metrics such as standard deviation analysis, proportion oftime (for example, hours per day) analysis, number of episodes per dayanalysis, maximum excursion during episode analysis (for example,measured in mg/dL), Mean Amplitude of Glycemic Excursions (MAGE)analysis, Lability Index analysis, Kovatchev Risk Score (based, forexample, on low/high glucose index), or Glycemic Risk AssessmentDiabetes Equation (GRADE) analysis, may be used to determine glycemicvariability based on continuously monitored glucose data received andstored from the analyte sensor 101, for example, by the processing andstorage unit 307 of the receiver unit 104/105 (FIG. 1).

The determined glycemic variability information may be output to theuser or the patient at the output/display unit 310 of the receiver unit104/105 to provide, for example, a snapshot of the diabetic condition ofthe analyte monitoring system user or patient based on glucose datareceived from the analyte sensor 101. In this manner, a user or apatient may conveniently track or monitor his/her diabetic conditionwhen sufficient glucose data has been received from the sensor.

FIG. 4 is a flowchart illustrating glycemic variability determination inaccordance with one aspect of the present disclosure. Referring to FIG.4, when the receiver unit 104/105 detects a request for glycemicvariability determination (410), one or more available metrics fordetermining glycemic variability information is retrieved (420), andpresented to the user/patient (for example, on the output/display 310 ofthe receiver unit 104/105). Upon receiving an indication selecting oneor more of the available metrics for glycemic variability determination(430), one or more parameters associated the selected metric forglycemic variability determination is retrieved (440). For example, inone aspect, when standard deviation analysis is selected as the metricfor determining the glycemic variability information, the associatednumber of glucose data points for determining the glycemic variabilityinformation using the standard deviation analysis is retrieved. Forexample, in one aspect, the standard deviation analysis may beconfigured to require 50 days of glucose data points to provide anaccurate glycemic variability information.

Referring to FIG. 4, as shown, it is determined whether the retrievedparameter meets the selected metric threshold (450). That is, in oneaspect, the processing and storage unit 307 of the receiver unit 104/105may be configured to retrieve stored glucose data points for the past 50days. When the glucose data points for performing the selected metricanalysis is determined to be sufficient (i.e., the receiver unit 104/105has glucose data points for at least the past prior 50 days), then theglycemic variability is determined based on the selected metric analysisand presented to the user or the patient, for example, output to theoutput/display 310 on the receiver unit 104/105 (460). On the otherhand, if the retrieved parameter does not meet the selected metricthreshold (450) (that is, there are less than 50 days of glucose datapoints available for the selected metric analysis), a failurenotification is generated and output to the user or the patient (470).

As described above, in one aspect, depending upon the selected metricfor glycemic variability determination, when the parameters associatedwith the selected metric is available (for example, the needed pool ofglucose data points spanning a predetermined time period which may beprogrammed in the receiver unit 104/105), the user or the patient may beprovided with a reliable and accurate information of the monitoreddiabetic condition. In one aspect, the parameters for each metric madeavailable to the user or the patient for determining glycemicvariability may be based on a pre-programmed threshold level for eachmetric to provide an acceptable accuracy level, and which may beadjusted or modified.

In a further aspect, the user input unit 303 (FIG. 3) of the receiverunit 104/105 may be provided with a dedicated button or selectableindicator for the glycemic variability determination. For example, a“hot key” may be assigned in a menu structure presented in theoutput/display 310 to provide user selectability of the glycemicvariability function. In this manner, in addition to receiving real timemonitored glucose information from the analyte monitoring system 100(FIG. 1), a user or a patient may readily, easily, and accuratelydetermine his/her glycemic variability. In yet a further aspect, theglycemic variability may be performed retrospectively at a remoteterminal such as the data processing terminal 105 which may include apersonal computer or a server terminal that has received and stored themonitored glucose data points.

FIG. 5 is a flowchart illustrating glycemic variability determination inaccordance with another aspect of the present disclosure. Referring toFIG. 5, when a request for glycemic variability information is received(510), the stored analyte data over a predetermined time period for themetric to determine glycemic variability is retrieved (520). Based onthe retrieved analyte data, the glycemic variability is assessed usingthe metric, for example, programmed or associated with the glycemicvariability determination function (530).

Thereafter, the determined glycemic variability information is generated(540) and then output to the user or the patient (550). That is, in oneaspect, when a user, a patient or a healthcare provider wishes to view asnapshot of the user or patient's diabetic condition, the receiver unit104/105 may be configured to perform glycemic variability determinationbased on pre-programmed metric. In one aspect, the pre-programmed metricmay be changed or varied as a user configurable parameter. In addition,the parameters associated with the pre-programmed metric may be userconfigurable or varied by, for example, the user, the patient or thehealthcare provider.

Experimental Results

The FreeStyle Navigator® Continuous Glucose Monitoring System was usedcontinuously for at least 70 days by ninety subjects. One-minute glucosedata over this time period were collected from each subject, and datafor the initial 20 days were excluded from the variability assessment asnot representative of the overall subject glucose variability. Theanalyte sensor was replaced every 5 days with a new sensor, andcalibration of each sensor at the scheduled calibration times wereperformed, for example, using an in vitro blood glucose meter. Eightglycemic variability metrics were calculated for each study subject. Adefined estimation accuracy requirement was predetermined and imposed inthe study to set the number of days of continuously monitored dataneeded for each metric determination.

The eight glycemic variability assessment metrics included standarddeviation analysis, proportion of time (for example, hours per day)analysis, number of episodes per day analysis, maximum excursion duringepisode analysis (for example, measured in mg/dL), Mean Amplitude ofGlycemic Excursions (MAGE) analysis, Lability Index analysis, KovatchevRisk Score (based, for example, on low/high glucose index), and GlycemicRisk Assessment Diabetes Equation (GRADE) analysis.

Based on subject analysis eligibility of more than 50 days ofcontinuously monitored glucose data, 68 of the 90 subjects wereeligible. For each subject, the true parameter value for each metricanalysis was based on the value of the subject's glucose data after 50days from the start of the continuous monitoring. The cumulative subjectvalue for the metric analysis was defined daily based on all cumulativedata for each day, where an acceptable parameter estimate was defined atcumulative value within +/−10% of the true parameter value. Also, theoptimal days of continuously monitored glucose data to acceptableaccuracy was defined for each subject as the first day at which thecumulative value remained within +/−10% of the subject's true parametervalue.

FIG. 6 shows a sample subject estimation of metric analysis based onMAGE with decreasing excursions, with a standard deviation factor of 1.0based on continuous glucose data collected over a time period ofapproximately 35 days after the initial 120 day period. FIG. 7 shows asample subject estimation of metric analysis based on GRADE usingcontinuously monitored glucose data over a time period of approximately30 days after the initial 20 day period, showing the mean, standarddeviation and the standard error.

FIG. 8 illustrates the overall glucose variability measures andestimation requirements for the study based on 90 subjects for each ofthe eight metric analysis performed. FIG. 9 illustrates the number ofdays of continuously monitored glucose data to attain the estimationrequirement of 10% with the exclusion of data from the initial 20 dayperiod for each of the eight metrics discussed above. FIGS. 10-17illustrate the results of each of the eight metric analysis,respectively, over the time period of approximately 30 days after theinitial 20 day period for glycemic variability assessment.

For example, FIG. 10 illustrates the glucose standard deviation analysisas the metric for glycemic variability assessment, FIG. 11 illustratesthe proportion of time analysis (in hours per day—in hypoglycemia orhyperglycemia) as the metric for glycemic variability assessment, FIG.12 illustrates the number of episodes per day analysis (in hypoglycemiaand hyperglycemia) as the metric for glycemic variability assessment,FIG. 13 illustrates the maximum excursion analysis for each hypoglycemicepisode or hyperglycemic episode as the metric for glycemic variabilityassessment, FIG. 14 illustrates the results of MAGE analysis as themetric for glycemic variability assessment, FIG. 15 illustrates theresults of the Lability Index analysis as the metric for glycemicvariability assessment, FIG. 16 illustrates the results of Kovachev RiskScore analysis as the metric for glycemic variability assessment showinglow blood glucose index (LBGI) and high blood glucose index (HBGI), andFIG. 17 illustrates the results of GRADE analysis as the metric forglycemic variability assessment, each of these figures based on datacollected from the subjects over the 30 day time period of continuousglucose monitoring.

In the manner described, in embodiments of the present disclosure,depending upon the metric analysis used for glycemic variabilityassessment, to achieve the desired accuracy level, the amount ofcontinuously monitored data may vary. Accordingly, one or moreparameters such as the number of days of available continuous glucosedata for determining glycemic variability based on a selected orpre-programmed metric analysis may be modified or adjusted to achievethe desired accuracy level of glycemic variability level.

As discussed, within the scope of the present disclosure, the particularmetric analysis (and/or associated parameters) for performing theglycemic variability assessment to illustrate a snapshot of a persons'diabetic condition may be defined or selected by the user, patient orthe healthcare provider, or pre-programmed into the system (for example,the receiver unit 104/105, a computing device or computing terminal, amobile telephone, a personal digital assistant, each configured toexecute instructions to perform the metric analysis). Further, thedevice or system may be programmed such that when the underlying dataavailable for the selected metric analysis is insufficient (for example,when there is not enough collected glucose data from the analytemonitoring device), the glycemic assessment analysis may be disabled orthe user, patient, or the healthcare provider may be notified that thereis insufficient data pool (or one or more conditions for one or moremetrics to determine the glycemic variability are not satisfied).

An apparatus in one aspect includes a memory configured to store one ormore executable instructions, a control unit operatively coupled to thememory and configured to retrieve the one or more executableinstructions for execution, an input unit operatively coupled to thecontrol unit for inputting one or more instructions to the control unitto execute one or more routines, and an output unit operatively coupledto the control unit for outputting one or more information, the outputunit including a glycemic variability indicator associated with adetermined variation in a glycemic level, where the glycemic variabilityindicator is provided to the output unit when the glycemic variationdata is determined to be sufficient for a selected one or more metric todetermine glycemic variability.

The control unit in one aspect may include one or more of amicroprocessor, an application specific integrated circuit, or a statemachine.

The one or more metric may include one or more of a standard deviation,MAGE, GRADE, Lability index, a number of glycemic variation episodeexcursions, a duration of each glycemic variation episode excursion, anaverage maximum excursion value, or a low/high blood glucose index.

Also, the one or more glycemic variation episode excursion may includedeviation from a predetermined hypoglycemic threshold parameter or froma predetermined hyperglycemic threshold parameter.

Further, each of the one or more metrics may include a predeterminedamount of data associated with the metric for glycemic variabilitydetermination.

In a further aspect, glycemic variability indicator may be provided tothe output unit only when the glycemic variation data is determined tobe sufficient.

The glycemic variation data may include a number of glucose datasamples. Also, the number of glucose data samples may be stored in thememory unit.

The apparatus may include a communication unit operatively coupled tothe control unit, the communication unit configured to send or receivedata to or from a remote location, and further, where the remotelocation may include one or more of a remote data processing terminal,an analyte monitoring system, a blood glucose meter device, a serverterminal, a personal digital assistant, a mobile telephone, or amessaging device.

The communication unit may be configured for wired or wirelesscommunication.

A method in one aspect may include receiving an instruction to determinea glycemic variation level, retrieving a stored metric for determiningthe glycemic variation level, retrieving one or more parametersassociated with the retrieved metric analysis, determining the glycemicvariation level based on the retrieved one or more parameters for theretrieved metric analysis, and outputting the determined glycemicvariation level when it is determined that the retrieved one or moreparameters associated with the retrieved metric analysis meets apredetermined condition.

The predetermined condition may include a predetermined stabilizationlevel.

The determined glycemic variation level may be output only when it isdetermined that the retrieved one or more parameters meets thepredetermined condition.

The predetermined condition may include availability of the number ofglucose data over a predetermined time period.

The predetermined time period may be associated with the retrievedmetric analysis.

Also, the glycemic variation level may be output as one or more of anaudible indication, a visual indication, a vibratory indication, or oneor more combinations thereof.

The method may also include storing the determined glycemic variationlevel.

Additionally, the method may include outputting an indication ofunsuccessful glycemic variation level when it is determined that theretrieved one or more parameters associated with the retrieved metricanalysis does not meet the predetermined condition.

Again, the one or more metric analysis may include one or more of astandard deviation analysis, MAGE analysis, GRADE analysis, Labilityindex analysis, a number of glycemic variation episode excursionsanalysis, a duration of each glycemic variation episode excursionanalysis, an average maximum excursion value analysis, or a low/highblood glucose index analysis.

An apparatus in a further aspect of the present disclosure may include astorage unit for storing one or more executable instructions, amicroprocessor operatively coupled to the storage unit for accessing theone or more executable instructions, which, when executed receives aninstruction to determine a glycemic variation level, retrieves a storedmetric for determining the glycemic variation level, retrieves one ormore parameters associated with the retrieved metric analysis,determines the glycemic variation level based on the retrieved one ormore parameters for the retrieved metric analysis, and outputs thedetermined glycemic variation level when it is determined that theretrieved one or more parameters associated with the retrieved metricanalysis meets a predetermined condition.

In some embodiments, a method is provided comprising receiving aninstruction to determine a glycemic variation level associated with aglycemic variation metric, retrieving one or more parameters associatedwith the glycemic variation metric, retrieving stored glycemic databased on the one or more retrieved parameters, determining if theretrieved glycemic data meets one or more predetermined conditions basedon the one or more parameters associated with the glycemic variationmetric, determining the glycemic variation level associated with theglycemic variation metric based on the retrieved one or more parametersand the retrieved glycemic data, when it is determined that the one ormore predetermined conditions are met, outputting the determinedglycemic variation level, and outputting a notification when it isdetermined that the one or more predetermined conditions are not met.

Certain aspects include that the parameters associated with the glycemicvariation metric include a value for a predetermined time period.

Certain aspects include that the parameters associated with the glycemicvariation metric include a value for a minimum number of glucose dataover a predetermined time period.

Certain aspects include that the parameters associated with the glycemicvariation metric include a value for a minimum number of days withavailable glucose data within a predetermined time period.

Certain aspects include that the determined glycemic variation level isoutput as one or more of an audible indication, a visual indication, avibratory indication, or one or more combinations thereof.

Certain aspects include that the determined glycemic variation level isoutput as a graphical representation of glycemic variation over time.

Certain aspects include that the notification is output as one or moreof an audible indication, a visual indication, a vibratory indication,or one or more combinations thereof.

Certain aspects include storing the determined glycemic variation level.

Certain aspects include that the glycemic variation metric includes oneor more of a standard deviation analysis, Mean Amplitude of GlycemicExcursions (MAGE) analysis, Glycemic Risk Assessment Diabetes Excursion(GRADE) analysis, Lability index analysis, a number of glycemicvariation episode excursions analysis, a duration of each glycemicvariation episode excursion analysis, an average maximum excursion valueanalysis, or a low/high blood glucose index analysis.

In some embodiments, an apparatus is provided comprising one or moreprocessing units, and a memory for storing instructions which, whenexecuted by the one or more processing units, causes the one or moreprocessing units to receive an instruction to determine a glycemicvariation level associated with a glycemic variation metric, to retrieveone or more parameters associated with the glycemic variation metric, toretrieve stored glycemic data based on the one or more retrievedparameters, to determine if the retrieved glycemic data meets one ormore predetermined conditions based on the one or more parametersassociated with the glycemic variation metric, to determine the glycemicvariation level associated with the glycemic variation metric based onthe retrieved one or more parameters and the retrieved glycemic data,when it is determined that the one or more predetermined conditions aremet, to output the determined glycemic variation level, and to output anotification when it is determined that the one or more predeterminedconditions are not met.

Certain aspects include that the parameters associated with the glycemicvariation metric include a value for a predetermined time period.

Certain aspects include that the parameters associated with the glycemicvariation metric include a value for a minimum number of glucose dataover a predetermined time period.

Certain aspects include that the parameters associated with the glycemicvariation metric include a value for a minimum number of days withavailable glucose data within a predetermined time period.

Certain aspects include that the determined glycemic variation level isoutput as one or more of an audible indication, a visual indication, avibratory indication, or one or more combinations thereof.

Certain aspects include that the determined glycemic variation level isoutput as a graphical representation of glycemic variation over time.

Certain aspects include that the notification is output as one or moreof an audible indication, a visual indication, a vibratory indication,or one or more combinations thereof.

Certain aspects include that the memory for storing instructions which,when executed by the one or more processing units, further causes theone or more processing units to store the determined glycemic variationlevel.

Certain aspects include that the glycemic variation metric includes oneor more of a standard deviation analysis, Mean Amplitude of GlycemicExcursions (MAGE) analysis, Glycemic Risk Assessment Diabetes Excursion(GRADE) analysis, Lability index analysis, a number of glycemicvariation episode excursions analysis, a duration of each glycemicvariation episode excursion analysis, an average maximum excursion valueanalysis, or a low/high blood glucose index analysis.

In some embodiments a method is provided comprising receiving aninstruction to determine a glycemic variation level associated with aglycemic variation metric, retrieving one or more parameters associatedwith the glycemic variation metric, retrieving stored glycemic databased on the one or more retrieved parameters, determining if theretrieved glycemic data meets one or more predetermined conditions basedon the one or more parameters associated with the glycemic variationmetric, determining the glycemic variation level associated with theglycemic variation metric based on the retrieved one or more parametersand the retrieved glycemic data, when it is determined that the one ormore predetermined conditions are met, and outputting a notificationwhen it is determined that the one or more predetermined conditions arenot met.

Various other modifications and alterations in the structure and methodof operation of this invention will be apparent to those skilled in theart without departing from the scope and spirit of the invention.Although the invention has been described in connection with specificpreferred embodiments, it should be understood that the invention asclaimed should not be unduly limited to such specific embodiments. It isintended that the following claims define the scope of the presentdisclosure and that structures and methods within the scope of theseclaims and their equivalents be covered thereby.

1. (canceled)
 2. A computer-implemented method comprising: generatingglucose sensor data indicative of a host's glucose concentration using aglucose sensor; calculating a glycemic variability index value based onthe glucose sensor data; and providing output to a user responsive tothe calculated glycemic variability index value.
 3. The method of claim2, wherein the providing output comprises generating a report to a user,wherein the report includes a calculated GVI numerical value.
 4. Themethod of claim 2, wherein the providing output comprises triggering analert to a user when the GVI exceeds a predetermined threshold, whereinthe alert is one or more of an audible alert, visual alert and tactilealert.
 5. The method of claim 2, wherein the calculating isautomatically performed periodically on a defined window of time ofsensor data.
 6. The method of claim 2, wherein the calculatingcomprising calculating a plurality of GVI values based on the sensordata, wherein each of the GVI values is based on a different period oftime of the sensor data.
 7. The method of claim 2, wherein the method isperformed by a processor executing code embodied in a non-transitorycomputer-readable medium.